Learning hybrid bayesian networks by MML

Authors:
Rodney T. O'Donnell;Lloyd Allison;Kevin B. Korb
Affiliations:
School of Information Technology, Monash University, Clayton, Victoria, Australia;School of Information Technology, Monash University, Clayton, Victoria, Australia;School of Information Technology, Monash University, Clayton, Victoria, Australia
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

Citing 10
Cited 3

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Coding Decision Trees

Machine Learning
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Bayesian Artificial Intelligence

Bayesian Artificial Intelligence
Models for machine learning and data mining in functional programming

Journal of Functional Programming
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)

Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Learning Bayesian networks with restricted causal interactions

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A transformational characterization of equivalent Bayesian network structures

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning Bayesian networks with local structure

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Causal discovery with prior information

AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence

Review: learning bayesian networks: Approaches and issues

The Knowledge Engineering Review
Causal discovery with prior information

AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Formal-Transfer In and Out of Stroke Care Units: An Analysis Using Bayesian Networks

International Journal of Healthcare Information Systems and Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

We use a Markov Chain Monte Carlo (MCMC) MML algorithm to learn hybrid Bayesian networks from observational data. Hybrid networks represent local structure, using conditional probability tables (CPT), logit models, decision trees or hybrid models, i.e., combinations of the three. We compare this method with alternative local structure learning algorithms using the MDL and BDe metrics. Results are presented for both real and artificial data sets. Hybrid models compare favourably to other local structure learners, allowing simple representations given limited data combined with richer representations given massive data.